Metabolomics is the study of small molecules of both endogenous and exogenous origin, such as metabolic substrates and their products, lipids, small peptides, vitamins and other protein cofactors generated by metabolism, which are downstream from genes. This approach has received more attention in recent years as an ideal methodology to unravel signals closer to the culmination of the disease process. The compounds identified through metabolomic profiling represent a range of intermediate metabolic pathways that may serve as biomarkers of exposure, susceptibility, or disease. In short, it is a valuable approach for deciphering metabolic outcomes with a phenotypic change.
Until recently, metabolomics and other post-genomic platforms, such as proteomics and transcriptomics, have not been suitable for large-scale, high-throughput epidemiologic applications. Studies that employed metabolomics technologies have focused on toxicological, physiological, and disease responses in animal models and small-scale human studies. This has been due mainly to the limited capacity of the analytical platforms for sample throughput and the processing requirements for the enormous amounts of data created.
Improved sample preparation, robotic sample-delivery systems, automated data processing, and use of multivariate statistical and chemometric methods, with associated reductions in cost, are now allowing researchers to realize the potential for metabolic phenotyping in epidemiology. Investigators have begun to extend these studies to larger-scale population studies for biomarker discovery. With these studies comes the challenge of applying metabolomics technologies in a manner that generates meaningful results. Epidemiologists must strive to comprehensively understand the principles of metabolomics to determine when it is appropriate to use biomarkers identified using this technology, which includes the ability to determine when biomarkers have been validated sufficiently.